Evaluating a Novel Hybrid Deep Learning Model for Brain Tumor Diagnosis

Anamika Kumari; Prashant Kumar; Triloki Nath; Bibekanand Mondal; Kunal Kanti Hazra; Nitin Anand; Nitesh Kumar

doi:10.2139/ssrn.6062834

January 1, 2026Open Access

Evaluating a Novel Hybrid Deep Learning Model for Brain Tumor Diagnosis

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Key Points

Improved accuracy in brain tumor diagnosis demonstrates the effectiveness of a hybrid deep learning model compared to traditional methods.
The hybrid model achieves a diagnostic accuracy increase of approximately 15% compared to existing algorithms.
Assessment conducted using various imaging datasets, including MRI scans, to validate model performance across diverse conditions.
Highlights the potential for enhanced diagnostic tools in medical imaging, emphasizing the need for continued development and validation.

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Implication

Observational analysis finds improved brain tumor diagnosis accuracy using a hybrid deep learning model, indicating significant advancements in medical imaging.

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Cite This Study

Kumari et al. (2026) studied this question.

synapsesocial.com/papers/69a75ff4c6e9836116a2c53a https://doi.org/10.2139/ssrn.6062834

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Evaluating a Novel Hybrid Deep Learning Model for Brain Tumor Diagnosis

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Evaluating a Novel Hybrid Deep Learning Model for Brain Tumor Diagnosis

Key Points

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Implication

Discussion

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